Data Engineer

Kitman Labs is the performance intelligence company, disrupting and transforming the way the sports industry uses data to unlock the potential of the world’s top athletes.

Driven by a passion to innovate in the areas of sports performance, analytics and user experience, we have assembled a team of the industry’s top data scientists, sports performance scientists, product and software designers and engineers.

Kitman Labs’ advanced operating system, the Intelligence Platform (iP) is now used by over 2000 teams in 50 leagues on 6 continents, including the NFL, Premier League, National Women’s Soccer League and MLS.

Data Engineer

We're looking for a Mid-Level Data Engineer to join our team and help build and evolve our data platform. You'll work across analytics engineering, data pipelines, and data quality — collaborating closely with Engineers, Data Scientists, and Product to turn raw data into reliable, scalable foundations.

Analytics Engineering & Reporting

• Build and maintain BigQuery data models using Dataform, following medallion architecture patterns (Bronze/Silver/Gold)

• Contribute to Looker dashboards and LookML models, working alongside senior engineers and analysts

• Write performant, well-structured SQL for large-scale transformations in BigQuery

• Implement data quality checks using Dataform assertions and automated alerting

• Support data observability across the warehouse — monitoring pipeline health, data freshness, and anomaly detection

Data Pipelines & Ingestion

• Build and maintain robust Python data pipelines with testing, linting, and CI/CD integration

• Work with orchestration tooling (Cloud Composer / Airflow) to schedule and monitor workflows

• Develop familiarity with CDC concepts and event-driven ingestion patterns (Datastream, Pub/Sub)

• Containerise workloads with Docker for deployment on Cloud Run or similar GCP services

Data Science Collaboration

• Support Data Scientists in moving work from notebook to production pipeline

• Contribute to feature pipelines and data preparation for ML workloads

• Help bridge the gap between research prototypes and scalable, maintainable code

• SQL proficiency — comfortable writing complex, performant queries against large datasets in BigQuery

• Dataform experience — or strong dbt experience with willingness to work in Dataform; understanding of modular, version-controlled data transformation

• Python with an engineering mindset — clean, tested, linted code; comfortable with Git and CI/CD workflows

• GCP familiarity — hands-on experience with BigQuery is essential; broader GCP exposure (Cloud Storage, Cloud Run, Pub/Sub, Datastream) is a strong advantage

• Orchestration experience — hands-on with Cloud Composer, Airflow, or a comparable tool

• Data modelling fundamentals — dimensional modelling, Kimball principles, or medallion architecture patterns

• Docker basics — able to containerise and deploy data workloads

• Collaborative and communicative — able to translate business requirements into data models and work effectively with Analytics, Product, and Data Science stakeholders

• Pragmatic approach to AI tooling — comfortable using AI-assisted development to improve productivity and code quality

• Looker / LookML experience

• Familiarity with CDC concepts and tools (Datastream, Debezium)

• Exposure to ML frameworks or MLOps tooling (scikit-learn, MLflow, Vertex AI)

• AWS experience as a complement (Redshift, Glue, RDS) — we value engineers who can draw on cross-cloud perspective

• Curiosity about sports performance data